jiang lu

Abstract To address the challenges of cross-modal information fusion in high-dimensional multimodal medical data for cancer prognosis, this study presents a hybrid diagnostic accuracy model for cancer survival prediction, integrating quantum computing with classical deep learning in a retrospective analysis of bladder cancer patients.

Abstract Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues developed a framework based on the variational graph encoder, NYAN, to facilitate the prediction of molecular properties in computer-assisted drug design.

Abstract Bladder cancer is one of the most common and highly vascularized cancers. To better understand its genomic structure and underlying etiology, we conduct whole-genome and targeted sequencing in urothelial bladder carcinomas (UBCs, the most common type of bladder cancer). Recurrent mutations in noncoding regions affecting gene regulatory elements and structural variations (SVs) leading to gene disruptions are prevalent.